Pure Storage announced a 75-blade all-flash system that operates as one unit as well as an artificial intelligence engine called Meta that aims to make storage arrays more autonomous. The common thread: Storage is increasingly all about the software.

At Pure Storage's Accelerate customer powwow, the company outlined a bevy of software updates and features for big data, analytics and artificial intelligence workloads as well as multi-cloud management tools.

While Pure's approach to integrated hardware and software has resulted in strong demand and growth, the upshot is that software is what's driving all-flash arrays.

First, the hardware: Pure outlined a series of updates to its FlashBlade product line including a 17TB system to go with its 8TB and 52TB configurations. Another update allows Pure customers to manage up to 75 blades as one system. The company has also moved to 100-percent NVMe flash.

Matt Kixmoeller, vice president of product at Pure Storage, said FlashBlade launched with one chassis for 15 blades, but realized it would need to scale. "In the past year, our customers really pushed for large scale systems," he said.

The jump to 75 blades as a unit up from the seven today is due to updates to Pure's Purity operating system. Pure is using a proprietary networking module that allows up to five chassis to be managed as one system.

Pure's 17TB configuration is designed for smaller deployments and more reasonable per gigabyte costs. Supply chain shortages for NAND memory and solid state drives have squeezed the storage industry.

Among Pure's software updates, the most notable ones add up to an ongoing goal of "self-driving storage." Pure's systems are connected to its Pure1 cloud and collect 1 trillion data points a day, more than 7PB of telemetry data and thousands of connected arrays. This sensor network provides data to a new Pure1 global dashboard, which will be generally available in the third quarter, that aggregates information on a storage array fleet.

Pure1 also has a VM Visibility module that can identify virtual machines with latency issues and find problems as needed. These analytics have avoided more than 1,100 issues, according to Pure.

With those Pure1 building blocks, Pure rolled out META, a global predictive intelligence system that can be used to manage, analyze, and support its arrays. Kixmoeller said that Meta is really "an evolution of the IoT platform we built from day one since all of our systems had call home sensors."

Meta is also a realization that machine learning has to do the heavy lifting when it comes to understanding workload performance. Kixmoeller said digesting thousands of measurements to predict workloads is "the perfect problem for machine learning since AI can run scenarios over and over."

Kixmoeller also noted that it's interesting that machine learning is needed to predict workloads primarily for artificial intelligence. "Machine learning is the big use case that's driving flash adoption," he said.

META's first efforts revolve around provisioning just what is needed for customers. Pure1 META Workload DNA, another new launch, will write everything from I/O size to compression and dedupe rates to data reduction based on more than 1,000 measures.

Pure's artificial intelligence engine learns from the entire customer base to predict how workloads will turn out. META will also power a Workload Planner module that will forecast load and capacity per FlashArray, provide insights, and impact analysis on migration and growth.